An information assistant system for the prevention of tunnel vision in crisis management

In the crisis management environment, tunnel vision is a set of bias in decision makers’ cognitive process which often leads to incorrect understanding of the real crisis situation, biased perception of information, and improper decisions. The tunnel vision phenomenon is a consequence of both the challenges in the task and the natural limitation in a human being’s cognitive process. An information assistant system is proposed with the purpose of preventing tunnel vision. The system serves as a platform for monitoring the on-going crisis event. All information goes through the system before arrives at the user. The system enhances the data quality, reduces the data quantity and presents the crisis information in a manner that prevents or repairs the user’s cognitive overload. While working with such a system, the users (crisis managers) are expected to be more likely to stay aware of the actual situation, stay open minded to possibilities, and make proper decisions

Development of an ontology for aerospace engine components degradation in service

This paper presents the development of an ontology for component service degradation. In this paper, degradation mechanisms in gas turbine metallic components are used for a case study to explain how a taxonomy within an ontology can be validated. The validation method used in this paper uses an iterative process and sanity checks. Data extracted from on-demand textual information are filtered and grouped into classes of degradation mechanisms. Various concepts are systematically and hierarchically arranged for use in the service maintenance ontology. The allocation of the mechanisms to the AS-IS ontology presents a robust data collection hub. Data integrity is guaranteed when the TO-BE ontology is introduced to analyse processes relative to various failure events. The initial evaluation reveals improvement in the performance of the TO-BE domain ontology based on iterations and updates with recognised mechanisms. The information extracted and collected is required to improve service k nowledge and performance feedback which are important for service engineers. Existing research areas such as natural language processing, knowledge management, and information extraction were also examined

Assessing identity, redundancy and confounds in Gene Ontology annotations over time

MOTIVATION: The Gene Ontology (GO) is heavily used in systems biology, but the potential for redundancy, confounds with other data sources and problems with stability over time have been little explored. RESULTS: We report that GO annotations are stable over short periods, with 3% of genes not being most semantically similar to themselves between monthly GO editions. However, we find that genes can alter their 'functional identity' over time, with 20% of genes not matching to themselves (by semantic similarity) after 2 years. We further find that annotation bias in GO, in which some genes are more characterized than others, has declined in yeast, but generally increased in humans. Finally, we discovered that many entries in protein interaction databases are owing to the same published reports that are used for GO annotations, with 66% of assessed GO groups exhibiting this confound. We provide a case study to illustrate how this information can be used in analyses of gene sets and networks. AVAILABILITY: Data available at http://chibi.ubc.ca/assessGO. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Master of Science

thesisData quality has become a significant issue in healthcare as large preexisting databases are integrated to provide greater depth for research and process improvement. Large scale data integration exposes and compounds data quality issues latent in source systems. Although the problems related to data quality in transactional databases have been identified and well-addressed, the application of data quality constraints to large scale data repositories has not and requires novel applications of traditional concepts and methodologies. Despite an abundance of data quality theory, tools and software, there is no consensual technique available to guide developers in the identification of data integrity issues and the application of data quality rules in warehouse-type applications. Data quality measures are frequently developed on an ad hoc basis or methods designed to assure data quality in transactional systems are loosely applied to analytic data stores. These measures are inadequate to address the complex data quality issues in large, integrated data repositories particularly in the healthcare domain with its heterogeneous source systems. This study derives a taxonomy of data quality rules from relational database theory. It describes the development and implementation of data quality rules in the Analytic Health Repository at Intermountain Healthcare and situates the data quality rules in the taxonomy. Further, it identifies areas in which more rigorous data quality iv should be explored. This comparison demonstrates the superiority of a structured approach to data quality rule identification

Will this work for Susan? Challenges for delivering usable and useful generic linked data browsers

While we witness an explosion of exploration tools for simple datasets on Web 2.0 designed for use by ordinary citizens, the goal of a usable interface for supporting navigation and sense-making over arbitrary linked data has remained elusive. The purpose of this paper is to analyse why - what makes exploring linked data so hard? Through a user-centered use case scenario, we work through requirements for sense making with data to extract functional requirements and to compare these against our tools to see what challenges emerge to deliver a useful, usable knowledge building experience with linked data. We present presentation layer and heterogeneous data integration challenges and offer practical considerations for moving forward to effective linked data sensemaking tools

RedundancyMiner: De-replication of redundant GO categories in microarray and proteomics analysis

<p>Abstract</p> <p>Background</p> <p>The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such as GoMiner can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Two or more of the categories are often redundant, in the sense that identical or nearly-identical sets of genes map to the categories. The redundancy might typically inflate the report of significant categories by a factor of three-fold, create an illusion of an overly long list of significant categories, and obscure the relevant biological interpretation.</p> <p>Results</p> <p>We now introduce a new resource, RedundancyMiner, that de-replicates the redundant and nearly-redundant GO categories that had been determined by first running GoMiner. The main algorithm of RedundancyMiner, MultiClust, performs a novel form of cluster analysis in which a GO category might belong to several category clusters. Each category cluster follows a "complete linkage" paradigm. The metric is a similarity measure that captures the overlap in gene mapping between pairs of categories.</p> <p>Conclusions</p> <p>RedundancyMiner effectively eliminated redundancies from a set of GO categories. For illustration, we have applied it to the clarification of the results arising from two current studies: (1) assessment of the gene expression profiles obtained by laser capture microdissection (LCM) of serial cryosections of the retina at the site of final optic fissure closure in the mouse embryos at specific embryonic stages, and (2) analysis of a conceptual data set obtained by examining a list of genes deemed to be "kinetochore" genes.</p